Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

ABSTRACT

A novel RUL prediction approach for lithium-ion batteries using quantum particle swarm optimization (QPSO)-based particle filter (PF) is proposed. Compared to particle swarm optimization (PSO)-based PF, QPSO-based PF is proved to have a better performance in global searching and has fewer parameters to control, which makes QPSO-PF easier for applications. Moreover, fewer particles are required by QPSO-PF to accurately track the battery's health status, leading to a reduction of computation complexity. RUL prediction results using real data provided by NASA and compared with benchmark approaches demonstrates the superiority of the proposed approach.     

A New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle

The extended particle filter (EPF) assisted by the Takagi-Sugeno (T-S) fuzzy logic adaptive system (FLAS) is used to design the ultra-tightly coupled GPS/INS (inertial navigation system) integrated navigation, which can maneuver the vehicle environment and the GPS outages scenario. The traditional integrated navigation designs adopt a loosely or tightly coupled architecture, for which the GPS receiver may lose the lock due to the interference/jamming scenarios, high dynamic environments, and the periods of partial GPS shading. An ultra-tight GPS/INS architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The EPF is a particle filter (PF) which uses the extended Kalman filter (EKF) to generate the proposal distribution. The PF depends mostly on the number of particles in order to achieve a better performance during the high dynamic environments and GPS outages. The T-S FLAS is one of these approaches that can prevent the divergence problem of the filter when the precise knowledge on the system models is not available. The results show that the proposed fuzzy adaptive EPF (FAEPF) can effectively improve the navigation estimation accuracy and reduce the computational load as compared with the EPF and the unscented Kalman filter (UKF).     

粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究

针对非线性非高斯系统的剩余寿命（RUL）预测问题,本文提出了一种基于粒子滤波（PF）理论的设备剩余寿命预测方法。首先建立设备的非线性状态空间模型（含有未知的时变参数）,然后通过粒子滤波算法估计出设备状态的概率密度函数（PDF）,从而根据该PDF计算出设备的RUL。此外,计算设备RUL的期望值和95%置信区间,并对模型的预测效果进行评估,验证预测的有效性和准确性。最后通过齿轮箱的全寿命实验,对本文所提方法的有效性进行实例验证,将实验结果和传统的比例风险模型（PHM）预测结果对比分析,结果表明本文提出的剩余寿命预测方法要优于传统的PHM预测方法。     

Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the afore-mentioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.     

多传感器数据融合测试系统自测方法研究

针对数据融合测试系统在测试中的重要作用,论文研究数据融合测试系统的自测方法.提出一种基于原位自测法的数据融合测试系统自测体系框架结构;给出测试系统的自测控制过程的详细描述,其中包括自测控制过程的自测方法与自测控制过程的统计方法等;结合多雷达数据融合测试系统实施例对自测系统的功能与性能进行分析说明.试验表明,通过对数据融合测试系统的自测,可进一步保证了测试结果的准确性和有效性.     

Conditions required to achieve the apparent equivalence of adhered solid- and solution-phase voltammetry for ferrocene and other redox-active solids in ionic liquids

The voltammetry of ferrocene (Fc) and Fc+ in the room-temperature ionic liquid, 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM x PF6) has been studied when solid is adhered to glassy carbon or platinum disk electrodes. Due to the slow dissolution kinetics and small diffusion coefficients in the viscous BMIM x PF6 ionic liquid, it is possible to obtain voltammograms of adhered Fc or Fc+ solid that are essentially indistinguishable (except for the current magnitude) from the reversible solution-phase Fc(0/+) process widely employed to provide a reference potential scale. However, the nature of the voltammetry obtained from the adhered solid is governed by the thickness (mass of the solid) of the particle layer. The mechanism proposed to explain the equivalence to solution-phase data involves dissolution at the particle/ionic liquid interface and is supported by electrochemical quartz microbalance measurements and a numerical simulation. Extensive studies on other redox-active solids suggest that voltammograms of solid particles adhered to the electrode surface in contact with ionic liquids frequently exhibit classical behavior associated with solution-phase diffusion-controlled voltammetry. Consequently, the method of adhering microparticles onto an electrode surface can frequently provide an efficient method of establishing ionic liquid solution-phase redox data using extremely small quantities of solid.     

Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function (pdf) to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’ rule. It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system, and thus the importance density function can be used to approximate the true posterior density distribution. In Bayesian filtering, the nonlinear filter performs well when all conditional densities are assumed Gaussian. When applied to the nonlinear/non-Gaussian distribution systems, the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle filter-based approaches, such as the extended particle filter (EPF), and unscented particle filter (UPF), and also the Kalman filter (KF)-type approaches, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF) and CKF. Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.     

Comparison of Cellular Automaton and Phase Field Models to Simulate Dendrite Growth in Hexagonal Crystals

A cellular automaton (CA)-finite element (FE) model and a phase field (PF)-FE model were used to simulate equiaxed dendritic growth during the solidification of hexagonal metals.In the CA-FE model,the conservation equations of mass and energy were solved in order to calculate the temperature field,solute concentration,and the dendritic growth morphology.CA-FE simulation results showed reasonable agreement with the previously reported experimental data on secondary dendrite arm spacing (SDAS) vs cooling rate.In the PF model,a PF variable was used to distinguish solid and liquid phases similar to the conventional PF models for solidification of pure materials.Another PF variable was considered to determine the evolution of solute concentration.Validation of both models was performed by comparing the simulation results with the analytical model developed by Lipton-Glicksman-Kurz (LGK),showing quantitatively good agreement in the tip growth velocity at a given melt undercooling.Application to magnesium alloy AZ91 (approximated with the binary Mg-8.9 wt% Al) illustrates the difficulty of modeling dendrite growth in hexagonal systems using CA-FE regarding mesh-induced anisotropy and a better performance of PF-FE in modeling multiple arbitrarily-oriented dendrites growth.     

EKF和PF算法及其在目标跟踪中的应用

基于贝叶斯滤波的目标跟踪原理,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)和粒子滤波(ParticleFilter,PF)的基本思想和算法实现步骤。在非线性环境下对比分析了EKF算法和PF算法的估计精度,并给出两种方法的适用条件。EKF算法采用Taylor展开的线性变换来近似非线性模型,而PF算法采用一些带有权值的随机样本来表示所需要的后验概率密度。仿真结果表明,在强非线性非高斯环境下,PF算法的跟踪性能远优于EKF算法,当系统非线性强度不大时,EKF算法和PF算法的估计精度相差不大,但PF算法计算复杂,跟踪时间长,实时性差。     

基于惯性跟踪的手臂运动及脑波一致性分析

考虑到脑卒中患者的传统偏瘫康复手段依赖于康复医生的指导和康复器械的协助,忽略了患者的主动参与性,缺少支配肢体运动的大脑神经系统的直接参与,以手臂运动为对象,研究了人体手臂运动时各姿态的变化及相应的脑电信号的变化,以探究人体上肢不同频率运动时大脑神经系统的参与情况与脑电信号变化情况,并提出了混合式Kalman滤波的数据融合算法和基于盲源分离的去除眼电伪迹的方法。在此基础上设计了快速手臂伸曲和慢速手臂伸曲运动实验和快速、慢速两种任务的手臂伸曲运动想象实验。实验表明,手臂运动与手臂运动想象脑波信号具有一致性,从而验证了所提方法的有效性。     

ORPUS-1 - A pulsed superconducting solenoid

A recent series of reference designs for Tokamak Experimental Power Reactors (EPR's) has indicated that superconducting poloidal field (PF) coils will be necessary for successful operation of these devices. It would also be desirable to use superconducting PF coils in earlier tokamak fusion devices if such coils could be developed quickly enough. In this paper, the PF coil performance requirements are briefly reviewed and some implications for the coil design are developed. A small coil (stored energy 14 kJ) has been built using construction techniques similar to those which could be employed for PF coils. The coil has been charged at rates up to 2 T/sec. Both maximum field and charging rate were limited by available power supplies. Loss measurements were carried out during pulsed operation and data for hysteretic and eddy current loss are presented. The loss measurement system used allows considerable insight into the effects of conductor motion and training.     

大型氦低温系统中的杂质净化

EAST是一个全超导的托卡马克核聚变实验装置,磁体采用超临界氦迫流冷却.磁体在降温过程中对氦气的纯度有很高的要求,净化系统是整个低温系统的重要环节之一,以防各种杂质气体在低温下凝结固化威胁低温系统的稳定可靠运行.理论分析了氦气纯化的基本原理及固定床吸附器的吸附机理,对低温纯化器的运行进行了阐述,并介绍了杂质成分检测系统,以对净化效果进行评估,实验得知氦气净化系统能满足低温系统氦气高纯度的要求.     

热固性酚醛树脂基微滤炭膜的制备

实验合成了球形热固性酚醛树脂微粒,并以此为原料制备了微滤炭膜.炭膜的孔径分布结果表明,在原料粒度较小的情况下,所制得的炭膜孔径分布较窄,平均孔径和气体透量较小.对几种常见的粘结剂进行了筛选,当以甲基纤维素为粘结剂时,随着甲基纤维素用量的增加,微滤炭膜的孔径分布变窄,平均孔径及气体透量减小.炭化条件中炭化终温对炭膜性能的影响较大.     

基于修正时延粒子滤波的水声传感器网络目标跟踪

在水声传感器网络中,利用多个传感器节点探测到的方位信息进行目标跟踪是水下目标跟踪领域的一种新思路。由于水中声速的限制,信号到达各个节点的时间不是同步的,提出了一种修正时间延迟的方法,并将其与粒子滤波(PF)、扩展卡尔曼粒子滤波(EKPF)结合来解决该非线性跟踪问题。仿真分析表明修正时延后,算法的跟踪性能有较大提高;并且在相同条件下,EKPF的跟踪性能比PF好。     

无机填料对木粉/高密度聚乙烯复合材料性能的影响

目的考察无机填料的种类、粒径以及添加量对PF/HDPE复合材料力学性能和热稳定性的影响。方法以杨木纤维(PF)、高密度聚乙烯(HDPE)、BaSO4、CaCO3、云母粉为原料,采用熔融共混和注塑成型的方法制备PF/HDPE复合材料,进行力学、热重、扫描电镜测试分析。结果3种无机填料均改善了PF/HDPE复合材料力学及热稳定性能,填充CaCO3获得的复合材料性能优于填充BaSO4、云母粉获得的复合材料,并且随着填料颗粒粒径的减少,改善效果增强。填料的添加量需要保持在一定范围内,添加量过低或过高均会造成性能下降。结论添加CaCO3(质量分数为9%,3000目)制备的PF/HDPE复合材料具有最佳的力学及热稳定性。     

粒子滤波和独立分量分析的含噪信号盲分离算法研究

研究了两阶段含噪独立分量分析算法来解决含噪信号盲分离问题。第一阶段,通过粒子滤波实现对不含噪混合信号的估计,将含噪独立分量分析转化为不含噪的独立分量分析;第二阶段用现有的FastICA算法从估计的不含噪混合信号中提取出源信号。不含噪混合信号的时变自回归模型和含噪与不含噪混合信号之间的关系构造了动态的状态．空间方程。该方程的特点是多变量、过程和观测噪声不限于高斯分布,粒子滤波是解决该问题的有效方法。提出了解决含噪独立分量分析的PF＋FastICA算法,仿真试验表明所提出的算法性能优于相关文献的结果。     

基于跟踪起始准则的多基地声呐阵位优化算法

针对水下区域监视多基地声呐（如浮标等）发射、接收节点阵位布置问题,提出了一种基于跟踪起始准则的多基地声呐阵位优化算法。该算法首先在分布式和集中式两种多基地融合处理模式下,推导了目标跟踪起始概率计算准则,然后,基于区域目标成功跟踪起始覆盖率,建立了多基地声呐阵位优化数学模型,并采用粒子群算法来优化求解该非凸数学模型,从而得到全局最优的多基地声呐发射-接收阵位。仿真结果表明,在两种多基地融合处理模式下,优化后多基地声呐节点阵位,其区域目标跟踪起始覆盖率比典型经验方案有较大提高。     

Simultaneous light scattering and intrinsic fluorescence measurement for the classification of airborne particles

We describe a prototype laboratory light-scattering instrument that integrates two approaches to airborne particle characterization: spatial light-scattering analysis and intrinsic fluorescence measurement, with the aim of providing an effective means of classifying biological particles within an ambient aerosol. The system uses a single continuous-wave 266-nm ultraviolet laser to generate both the spatial elastic scatter data (from which an assessment of particle size and shape is made) and the particle intrinsic fluorescence data from particles in the approximate size range of 1-10-mum diameter carried in a sample airflow through the laser beam. Preliminary results suggest that this multiparameter measurement approach can provide an effective means of classifying different particle types and can reduce occurrences of false-positive detection of biological aerosols.     

A decentralized control scheme for orchestrating versatile arm movements in ophiuroid omnidirectional locomotion

Autonomous decentralized control is a key concept for understanding the mechanism underlying the adaptive and versatile behaviour of animals. Although the design methodology of decentralized control based on a dynamical system approach that can impart adaptability by using coupled oscillators has been proposed in previous studies, it cannot reproduce the versatility of animal behaviours comprehensively. Therefore, our objective is to understand behavioural versatility from the perspective of well-coordinated rhythmic and non-rhythmic movements. To this end, we focus on ophiuroids as a simple good model of living organisms that exhibit spontaneous role assignment of rhythmic and non-rhythmic arm movements, and we model such arm movements by using an active rotator model that can describe both oscillatory and excitatory properties. Simulation results show that the spontaneous role assignment of arm movements is successfully realized by using the proposed model, and the simulated locomotion is qualitatively equivalent to the locomotion of real ophiuroids. This fact can potentially facilitate a better understanding of the control mechanism responsible for the orchestration of versatile arm movements in ophiuroid omnidirectional locomotion.     

An ensemble model for engineered systems prognostics combining health index synthesis approach and particle filtering

Prognostics, in other words, remaining useful life (RUL) estimation is a core task of prognostics and health management (PHM). Reliable RUL predictions can reduce maintenance costs, improve production efficiency, and avoid unexpected downtime. Lots of models for RUL predictions have been proposed; however, noise and the nonlinear nature of degradation phenomena often leads to poor prognostics results, and the acquired engineered system data are usually subject to a high level of uncertainty. This makes the RUL estimation models less than satisfactory. Accurate RUL estimation and prediction not only rely on an accurate model but also depend on the adjustments of model parameters to track the variation. In this paper, an ensemble model combining the health index synthesis (HIS) approach and improved particle filtering (PF) is introduced. HIS approach was used to obtain the synthesized health index (SHI) for an engineered system with multiple sensors, which indicated the system's degradation model, while the improved PF approach was used to adjust the parameters of the degradation model obtained from the HIS approach and optimized the RUL estimation results. The performance of the prognostics approach introduced in this paper was demonstrated by using turbofan engine degradation data sets, which was supplied by NASA Ames, and results were compared with several usually used methods.